Examinando por Materia "Soil acidity"

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Biorremediación del suelo con acidez y metales pesados (As, Cd) en un ecosistema altoandino con influencia minera mediante biochar de estiércol de cuy y consorcios microbianos
(DEEV MINAS, 2026-04-24) Arias Arredondo, Alberto; Cruz Luis, Juancarlos Alejandro; López Rodríguez, Melina; Solórzano Acosta, Richard Andi
Los suelos de pastizales altoandinos en zonas con influencia minera (Pasco, Perú, 4125 m) presentan acidez y contaminación por arsénico (As: 5.77 mg kg⁻¹) y cadmio (Cd: 1.71 mg kg⁻¹), lo que genera riesgo de transferencia de metales a la cadena trófica ganadera. Evaluamos el efecto del biochar de estiércol de cuy inoculado con consorcios microbianos (Bacillus subtilis, Pseudomonas putida, Trichoderma sp.) sobre la inmovilización de As y Cd y la calidad nutricional de pastos nativos (Festuca dolichophylla, Carex sp.) y cultivados (Lolium perenne, Dactylis glomerata). Establecimos un experimento factorial 4×4 en bloques completos al azar (48 parcelas). Determinamos factores de bioconcentración (BCF) y calidad nutricional, analizando mediante GLM y PCA. Los resultados evidenciaron una interacción significativa especie×tratamiento. Destacó la reversión fenotípica de Festuca dolichophylla frente a As, que pasó de acumuladora (BCF>1) a exclusora efectiva (BCF<<0.01) bajo inoculación con Trichoderma sp. Asimismo, Trichoderma sp. y P. putida redujeron la translocación de Cd en Lolium perenne (BCF<<0.1), manteniendo niveles seguros para consumo animal. El uso sinérgico de biochar y consorcios microbianos mitigó la transferencia de metales y preservó la calidad nutricional del forraje.
Spatial Modelling of Soil Quality and Lime Requirement for Precision Management in Humid Tropical Coffee Systems
(MDPI, 2026-02-25) Díaz Chuquizuta, Henry; Mejia Maita, Sharon Yahaira; Mercado Chinchay, Ruth Lizbeth; Arroyo Julca, Michell Karolay; Ore Valeriano, Ruddy Adely; Díaz Chuquizuta, Percy; Manrique Gonzales, Luis Fernando; Sánchez Ojanasta, Martín; Quispe Matos, Kenyi Rolando
Soil heterogeneity and acidity are major constraints to Coffea arabica production in the Amazonian soils of Peru. This study developed a spatial predictive framework that integrates a weighted Soil Quality Index (SQIw) and geostatistical modelling (Regression–Kriging and Ordinary Kriging) to estimate lime requirements (LRs) and delineate management zones. A total of 69 coffee-cultivated soil samples were analysed, and spectral information (NDVI) was incorporated to estimate relative yield (RR). Multivariate analysis defined a Minimum Data Set (MDS) composed of exchangeable Na, available P, pH and silt percentage; the highest weights were assigned to P (Wi = 0.292) and pH (Wi = 0.276). SQIw exhibited wide variability (0.01–0.87; CV = 51.8%) and was grouped into five classes, with low (43.5%)- and very low (21.7%)-quality classes predominating. SQIw showed a strong relationship with RR (r = 0.64). Geostatistical models performed differently between localities: in Nuevo Huancabamba, Regression–Kriging improved prediction accuracy (SQIw: R² = 0.58; LR: R² = 0.396), whereas in San José de Sisa, Ordinary Kriging provided better fits only for LRs (R² = 0.32). Nuevo Huancabamba is dominated by moderate-to-high-quality soils (87.29%; SQIw > 0.6) and low lime requirements (74.94%; <0.84 t ha⁻¹), in contrast with San José de Sisa, where low-quality soils prevail (89.45%; SQIw < 0.4) alongside high LRs (75.26%; 2.54–7.13 t ha⁻¹). The resulting maps enable targeted interventions—precision liming and focused P fertilisation—to correct acidity and phosphorus deficiency, thereby improving input-use efficiency and enhancing the sustainability of Amazonian coffee systems.